Physical distancing and emergency medical services utilization after self-harm in Korea during the early COVID-19 pandemic: A nationwide quantitative study

Background People experienced various stress and psychological responses to the coronavirus disease 2019 (COVID-19) pandemic. This study aimed to examine the changes in emergency medical services (EMSs) utilization by self-harm patients in early pandemic and the impacts of physical distancing measures on the EMSs utilization by self-harm patients. Methods Data for all patients presenting to emergency departments (EDs) after self-harm injuries including self-poisoning were collected from the National ED Information System (NEDIS). Characteristics of patients in two study regions (urban versus rural) were compared. Weekly and annual ED visit rates after self-harm (VRSH) per 100,000 population were calculated. Mobile phone mobility index (MPMI) was calculated by dividing a region’s aggregated mobile phone mobility by mid-year population. Joinpoint regression analysis was conducted to assess changes in 2020 over pre-pandemic years. Test for presence of joinpoint at the end of 2019 was performed. A cross-correlation function was used to estimate the maximal morphological similarity and lag time between changes in MPMI and VRSH. Results In 2020, in early phases of the pandemic, there was a moderate decline in self-harm-related ED visits to 30,797 from a continuously increasing trend seen in previous years. However, proportions of young people (50.1%) and females (62.3%) increased over previous years. VRSHs among women and young people aged 15–34 years showed higher levels in 2020 than in previous five years. There was a significant decrease in the proportion of patients transported directly from the scene. In addition, there was a polarization of mental state upon ED arrival from alert and unresponsive. The median correlation coefficient between MPMI values and VRSH values was 0.601 (interquartile range [IQR]: 0.539–0.619) in urban regions and 0.531 (IQR: 0.454–0.595) in rural regions, showing no statistically significant difference between the two. Conclusion Physical distancing measures adopted to prevent the spread of transmittable diseases following the pandemic had the effect of decreasing ED visits due to self-harm. When the pandemic has ended, and daily life has been restored, it will be particularly important to pay attention to the increased numbers of self-harm patients expected to visit EDs compared to during the pandemic.


Introduction
Comment 1: 1) The authors reference other studies that have explored emotional distresses as a result of COVID-19, but they do not refer to any studies from Korea. More description of the situation in Korea during pandemic and the lockdown would be helpful. It could help to get inside to how the population in Korean reacted to the pandemic.
Response: Thank you for your valuable comments. We cited Korean studies that described the effects of pandemic and the lockdown measures for young and elderly adults.

Comment 16: Results
Be consistent in using self-harm or self-injury. In the title the authors use selfharm, then the word should be self-harm. Thank you. We unified the term.
Comment 17: Results 1 sentence in section under the table: there is something wrong with this sentence "……was higher than in 2019 (+2.35%) than during the same period in 2019". Changes in the incidence of self-harm during the early stages of the pandemic Comment 18: Results Section 2: In this sentence: "Compared to the previous five years, the proportion of self-harm patients aged 15 to 34 increased significantly in 2020 (P< 0.046)…". -You cannot merge 2 age groups to one age group when you have 2 age groups with different P-values in the Table 2. 15-24 and 25-34 (p<0.001 and 0.046, respectively). We have revised. Thank you.
Comment 19: Results "…. whereas those aged 35 and older decreased significantly (P < 0.050)". Same problem here. P-values are different for each age group. You can write (Table 2).

Response:
We have revised. Please review again.
Comment 20: Results Section 2 sentence 4: delete the incidence of stubbing injuries; The authors use incorrect wording of incidence of stubbing : They do not calculate incidence of stubbing. The correct word is self-harm by stabbing; Response: It was corrected as "the proportion of stabbings increased to 29.5%," Comment 23: Results Section 2 sentence 7: During the pandemic era, 93.5% of patients walked directly to the ER, compared to over 99% in the past (P < 0.001). If you round the number in the past, then you should also round the number during the pandemic. Beside it is almost 100% when rounding off the decimal numbers. You can write (99.7 -99.8%) instead.

Response:
My sincere thanks go out to you for your comments. Updates have been made.

Comment 24: Discussion
The authors should discuss that fear of being infected with corona can also lead to reduction in ED visits.

Response:
We have discussed on page 16, section "The results of this study found that ~ And the implementation of measures that can monitor these changes in real time and take countermeasures is therefore necessary."

Comments from Reviewer 3
Summary: This study aimed to compare the incidence, proportion, demographic and other clinical characteristics of ED visits for self-harm between rural and urban regions in South Korea, during 2020 and 2015-2019. The authors found an overall decrease in ED visits for self-harm in 2020 compared to previous years, which was correlated with mobile mobility measures (a measure of social distancing) suggesting social distancing practices led to a decrease in usage of ED services. They also report ED visits for self-harm exhibited higher proportions of females and young adults in 2020 compared to previous years. While the aims of this study are important and worth investigating, significant revisions to the methodological and statistical approaches are required to accurately produce and interpret the findings.
Please accept my sincere thanks for reviewing our paper. I have responded to each of the comments below, so please read them and review the manuscript.

Comment 1: Introduction:
The introduction states "the study intended to verify that the utilization of emergency care after self-harm increased as physical distancing measures were implemented". Is there more background information to support why the authors made this hypothesis? Most previous literature show decreased use of emergency services due to COVID-19 precautions (including the results of this study!).
Response: One of the major comparisons is between individuals from rural and urban regions. Could the authors present more background on expected differences between these regions?.

Comment 2: Methods
• Measurements: could the authors differentiate between accidental self-harm from intentional self-injury? Intentional-self harm can also occur without suicide intent and this is quite different from accidental self-harm that may not have a significant psychological component. Since one of the goals of this study is to determine how to allocate mental health services to prevent intentional self-harm, it would be better to exclude cases of accidental self-harm from the analyses (or compare the results with and without excluding them).

Response:
We defined the self-harm as "The term "self-harm" refers to nonfatal intentional self-injury or self-poisoning, regardless of the apparent motivation or suicidal intent" in Introduction section.

Comment 3: Methods
Mobile mobility index -What distance away from the home site would count as one mobile mobility point?
Response: One-mobile phone mobility is recorded when an individual moves from one village (residence site: staying from midnight to 6 a.m.) to another village (the smallest administrative unit in Korea). The MPMI was calculated by aggregating the total mobile phone mobility and dividing it by the mid-population of each provision. MPMI includes movement statistics for all SK Telecom subscribers aged 15 or older on a weekly basis. This is the data that the National Statistical Office of the Republic of Korea collects and discloses to the public.

Comment 4: Methods
• When comparing urban vs. rural areas, what time period was selected as "early pandemic"? Did this period correspond to significant enforcement of social distancing measures?
Response: Yes, it was. We included the early pandemic period of 2020 in our analysis, and strong distancing regulations were implemented in South Korea during this time period. Measures including a ban on school attendance and a ban on eating in restaurants and cafes were implemented. And the intensity of bans was adjusted to confirmed cases in each province. Therefore, we collected and analyzed the mobile phone mobility index for each province every week.

Comment 5: Methods
Two-sample t-tests (comparing rural and urban regions): p-values will need to be adjusted for multiple testing.

Response:
We conducted a t-test with all independent variables in the syntax and analyzed results were corrected using the Bonferroni method. We added the related contents to the methods section.

Comment 6: Methods
Chi-square test: omnibus chi-square analyses were done for measurements like injury mechanism and indicate overall significant differences in proportions between regions. However, post-hoc chi-square tests may be required to identify individual levels that significantly differ (for example -is choking really significantly less frequent in rural compared to urban areas, or is the difference in poisoning and stabbing driving the omnibus significance?) (1). Of course, adjustments for p-values must also be done to correct for multiple comparisons.
Response: Based on your recommendation, we conducted a chisq post-hoc test for each subgroup of Table 1 after conducting the chisq test on the entire table. It would be greatly appreciated if you could review it.

Comment 7: Methods
Correlation-shift-function: could the authors provide more details about the parameters selected using this tool? For example, did the authors perform linear or circular correlations? As well, could the authors provide citations for other papers that have used this software for similar analyses?

Response:
We ran the correlation shift app (https://www.originlab.com/FileExchange/details.aspx?fid=466) using Origin Pro (Origin Lab, Northampton, MA), but we suspect the correlation coefficient values were excessively measured. Therefore, we sent queries to OriginLab (You may refer to the bottom of this page for more information about the query: https://www.originlab.com/FileExchange/details.aspx?fid=466), but were not able to receive an answer. Consequently, the authors used R to test the similarity of two curve (MPMI and VRSH) and lag times between the two groups by performing cross-correlation functions in R. Finally, we re-performed statistical analysis R program and have updated all statistical values in the results section. Please review again Comment 8: Methods What were the effects of selecting other lag-times? How significantly different were the correlations and do these impact confidence in the interpretations?
Response: I have attached a cross correlation (CC) graph for your reference. While the correlation coefficients of VRSH and MPMI will differ from region to region, the CC analysis will generally produce similar graphs. Besides, the lag time is not selected by the authors. Instead, the R program automatically selects the point where maximum height CC appears while moving VRSH and MPMI left and right. The highest CC may be low and the 95% confidence interval may be wide if you choose a different lag time.

Comment 12: Results
Here, the p-value is the omnibus chi-square test, not the significance of specific comparisons for "alert mental state" between rural vs urban areas.
Response: I apologize for the error. I conducted a Chisq Post-hoc test and attached the results. As you stated, there is no difference in the level of consciousness between the two regions (P>0.243).
Comment 13: Results SF1 -general trends were described for this figure. However, real statistical tests are required to make conclusions about differences in mobile mobility each week.
Response: Repeated measured ANOVA was performed and the results were added to the results section. S1 Fig. has been changed to Figure 1 and moved into the text. Thank you.

Comment 14: Results
This data can actually be used to define specific time-periods of low mobile mobility. It would be interesting to compare intentional self-injury between these time-windows.
Response: There are 16 provinces in Korea. Local governments adjusted the strength of the social distancing measure based on COVID-19 epidemic status. There may were a change in MPMI as a result. If you want to analyze only the MPMI reduction time-windows separately, it should be selected separately for each province. It is also critical to define the reduction criteria. In my opinion, there are too many variables to consider. The authors tried to find relationship between MPMI as a distancing parameter and VRSH using crosscorrelation. Because it was most effective way to find CC and lag time mathematically is by moving the curves of MPMI and VRSH for each local region from one side to the other. I'd appreciate it if you could take this into consideration.

Comment 15: Results What does the line represent?
Response: It appears that Figure S1 contains a large amount of meaningful data, so I have changed Figure 1. Additionally, the graph lines show the means and error bars present 95% CI for VRSH based (we changed error bars from SEs to 95% confidence intervals). In my opinion, we can realize a clearer picture of the changes because of the lockdown and time period.

Comment 16: Results
What is the mobile mobility ratio? What two values are used to make the ratio?
In SF1, the 2020/2019 ratio represents the change in MPMI in 2020 as compared to MPMI in 2019. This graph line shows how MPMI has decreased for each week in 2020 as compared to 2019.

Response: Even in normal years, MPMI varies according to the four seasons and the vacation season.
When we first tried the analysis, we tried to show the ratio by dividing MPMI in 2020 by MPMI in 2019. As a result, the ratio has been removed, as the values from the two regions have been presented in graphs for each of 2019 and 2020 (Fig. 1). I would appreciate it if you could review this again. Table 2 -unclear what tests the p-values for each row represent. If these are for t-tests, which years were specifically compared? The results section discusses differences in proportions for some of these results, suggesting chi-square tests were done. If so, why is there a pvalue for each row?

Comment 17: Results
Response: It makes sense to use the Chisq test to determine whether the overall distribution varies significantly by group from 2014 to 2020. However, the authors are interested in determining whether there was a significant change in 2020 relative to the previous five years. Therefore, we compared the proportion (%) in 2020 to the average value of the previous year using a one-sample t-test. Therefore, it is correct that each row has a p-value. And Comment 18: Results For falling and machine related injuries, were these accidents or purposeful selfharm? If these were accidental, it would be better to remove them from the analyses… The study does not include unintentional injury cases, as "machine-related" and "falling" self-harm are only included if they are confirmed by triage physicians or nurses as self-harm. The research method indicates that all subjects included in this study are "intentionally self-injured patients". When collecting NEDIS data, all unintentional injuries or traumas are sent as "unintentional" to the server. Consequently, only "purposeful self-harm" was entered into the research target.
Response: Absolutely correct. Unintentional accidents are coded with a different value. So only selfharm cases are classified as "intentional self-harm" at ED arrival. Table 3 -again, was data from 2020 compared to each of the previous years or an average from the previous years? What was the standard deviation for the 2020 data? Were the units weekly incidence of self-harm per 100 k people?

Comment 19: Results
Response: Incidence is wrong term. Therefore, we changed to correct term "weekly ED visit rate after self-harm per 100,000." in entire manuscript. The SD value could not be obtained for 2020 data because of a single data set. The 2020 values were compared with data from the past five years using a student t-test or a Mann-Whitney U test.
Comment 20: Results T-tests need to be corrected for multiple comparisons for both table 1 and 3. P-value in table 1 is from chisq test. And we did chisq post-hoc test according to your advice and added the results.

Response:
We used the one sample test comparing the 2020 value with the previous year's average in table 2 and3. A multiple comparison was not conducted. I would appreciate it if you could take this into consideration and again provide me with your advice. Fig 1, Fig 2, Table 4: The correlation seems extremely high compared to other studies (2). Why is it so high?

Comment 21: Results
Response: We ran the correlation shift app (https://www.originlab.com/fileExchange/ details.aspx?fid=466) using Origin Pro (Origin Lab, Northampton, MA), but we suspect the correlation coefficient values were excessively measured. Therefore, we sent queries to OriginLab, but were not able to receive an answer. Consequently, the authors used R to test the similarity of two curve (MPMI and VRSH) and lag times between the two groups by performing cross-correlation functions. Finally, we have updated all statistical values in the results section. Please review again  1.85 ,1.64 ,1.68 ,1.78 ,1.84 ,1.84 ,1.93 ,1.91 ,1.92 ,1.97   Most of the highest correlation occurred around 0-1 week, and the change in correlation according to lag time can be seen in the output data above or the figure below. I would appreciate it if you could refer to them.

Comment 23: Results
How can the average correlations and time-lags be lower in males and females compared to the overall population? Are they statistically lower (you could use a one-sample t-test here).
Response: Thank you for taking the time to review this in detail. In the process of transferring the Origin program output, the CC values and lag time values of the male and female were misrepresented as 0.000 (default). We have updated the original data. It would be greatly appreciated if you could review Table 4 and reevaluate it.
We compared CC value and lag time for two groups (Urban areas (7 metropolitan cities) and rural areas (9 provinces) in Korea) by student T-test.

Comment 24: Discussion
The results suggest an increasing trend in self-injury in young people 15-25 over in 2020 compared to previous years. However, there was also a large increase from 2017-2018 in this age group. Is it possible that other factors are contributing to this increase besides the COVID-19 restrictions?
Response: That is a valid point, in my opinion. It is possible that factors such as university entrance examination systems and changes in social welfare may affect self-harm among students aged 15 to 25. There was also a political disruption (transfer of power from the right-wing government to the left-wing government) in 2017. However, the main focus of this study is to compare the 2020 early pandemic period with the previous years. If ED visits after self-harm have increased significantly in the previous years (2017 vs 2018), the one-sample T-test result should show no significance comparing 2020 with previous years. Considering these political factors, it is reasonable to explain this increase as COVID-19 as a result of a significant increase in the 15-25 groups in 2020.

Comment 25: Discussion
The results also suggest an increase in incidence and proportion of self-injury in females in 2020 possibly due to COVID-19 restrictions. However, an even larger increase seemed to have occurred between 2017-2018. Since it is unclear if the p-value corresponds to an omnibus test or compared to the average of all years prior to 2020, I'm not sure the authors can make conclusions about incidence.
Response: That is correct, thank you. In 2017 and 2018, the population standardized visit rate for women increased significantly by 19.29% and 13.69% compared to the previous year. There was, however, a reduction in the increase in 2020 to 7.25%. We believe that lockdowns and social distancing will reduce emergency department visit rates after self-harm in 2020, which supports our argument. I would appreciate it if you could consider this and review it again.

Comment 26: Discussion
While the proportion of females seemed to have increased in 2020, the interpretation may be very different depending on if the incidence is different or not. In Table 2, the proposition of female patients increased significantly in 2020. In addition, it can be seen that the population standardized visibility rate in table 3 also decreases in men, but rather increases in women.
I have concluded that the change in the proportion of females can be explained by the unique increase of population standardized visit rate in the female group.
Response: There are some points on which I agree with you. As a result, however, if population standardized VRSH is to be directly related to changes in female VRSH in the entire population, we must assume that sex ratios in all provinces are the same. Because we can't confirm it like that, I don't think we can assure the crude female visit ratio only with population standard VRSH. Therefore, we expressed it on page 15 as follows. "In this study, the proportion of females among selfharm patients and the population standardized VRSH among females increased significantly from previous years." In my opinion, it would be appropriate to present both findings. It would be appreciated if you could review it again after taking this into consideration.

Comment 27: Discussion
The authors note that neglecting to get ED treatment after mild self-harm injury is unlikely to occur based on the proportion of alert mental status patients. Can the authors explain this interpretation more? Given the proportion of self-harm ED was lower for older-adults, it's possible that older patients who exhibited intentional self-harm may have perceived the risk of acquiring COVID-19 to be greater than the possible consequences of mild intentional self-harm and chose not to go to the ED. If this was the case, then mental health services should be allocated to older adults as well.
Response: Regarding your concerns, thank you very much. On page 16, we describe the related contents in the second sentence. Patients who self-harm mildly may neglect to visit the ED. Despite that, we attempted to demonstrate that the phenomenon was not significant in this study by citing related studies (reference 56-58). We request that you review this again.